2024 2nd International Workshop on Signal Processing and Machine Learning (WSPML 2024)

★2024 2nd International Workshop on Signal Processing and Machine Learning (WSPML 2024)---Ei Compendex & Scopus—Call for paper  

June 7-9, 2024｜online｜Website: www.wspml.org

★WSPML 2024 presents researchers, engineers, and academics with an unprecedented opportunity to associate and interact with some of the foremost experts in the field of Signal Processing and Machine Learning from around the globe. WSPML 2024 aims to be the must-visit conference of the year for the field, and will be the ideal forum for the exchange of ideas and innovations that promise to change the face of the industry. For researchers, engineers, or industry professionals, this conference could mark an important point in your career.

★Publication and Indexing

All accepted and registered full papers will be published conjunctly with other conference proceeding in the form of digital conference proceeding and submitted to major citation databases like EI Compendex, SCOPUS, Google Scholar etc. for reviewing and indexing. Excellent Papers from WSPML 2024 will be recommended to be submitted for publication in journal.

★Keynote Speakers

Prof. Michalis Matthaiou——Queen’s University Belfast, United Kingdom

Prof. Chik Patrick Yue——The Hong Kong University of Science and Technology, China

★Program Preview/ Program at a glance

June 7: Registration + Icebreaker Reception

June 8: Opening Ceremony+ KN Speech+ Technical Sessions

June 9: Technical Sessions+ Half day tour/Lab tours

★Paper Submission

Submit Via CMT: https://cmt3.research.microsoft.com/WSPML2024

★CONTACT US

Ms. Riva H. W. Wong

Email: [email protected]

Website: www.wspml.org

★Topics of Interest

Signal, Image and Video Processing

Digital Signal Processing

Feature Extractions

Image and Multidimensional Signal Processing

Image/Video Processing and Coding

Mining Images and Texture

Mining Images in Computer Vision

Multimodal Data Fusion

Graphical Models and Kernel Methods

Deep Learning, Machine Learning, Pattern Recognition, Artificial Intelligence

Learning Theory

Machine Learning Methods

Learning of Action Patterns

Neural Methods

Classification Techniques

Data-Driven Adaptive Systems

Data-Driven Models